Artificial Intelligence (AI) is now ubiquitous in daily life, significantly impacting society by supporting decision-making. However, in many application areas, understanding the rationale behind AI decisions is crucial, highlighting the need for explainable AI (XAI). AI algorithms often lack transparency, making it hard to understand their inner workings. This work presents an overview of XAI solutions for decision support in mobility context. It addresses the complexity of explaining decision support models by offering explanations in various formats tailored to different user profiles. By integrating language models, XAI models may generate texts with varying technical detail levels, aiding ethical AI deployment and bridging the gap between complex models and human interpretability. This work explores the need for flexible explanation formats, supporting varied user profiles with graphical, textual, and tabular explanations. By integrating natural language processing models personalized explanations that are accurate, understandable, and accessible to a diverse audience can be generated. This study ultimately aims to support the task of making XAI robust and user-friendly, boosting its widespread use and application.